
'''simple docstring'''






import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


UpperCAmelCase_							: Union[str, Any] =							['bart.large', 'bart.large.mnli', 'bart.large.cnn', 'bart_xsum/model.pt']
UpperCAmelCase_							: List[Any] =							{'bart.large': BartModel, 'bart.large.mnli': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('0.9.0'):
 raise Exception('requires fairseq >= 0.9.0')


logging.set_verbosity_info()
UpperCAmelCase_							: Any =							logging.get_logger(__name__)

UpperCAmelCase_							: Any =							' Hello world! cécé herlolip'

UpperCAmelCase_							: Optional[int] =							[
    ('model.classification_heads.mnli.dense.weight', 'classification_head.dense.weight'),
    ('model.classification_heads.mnli.dense.bias', 'classification_head.dense.bias'),
    ('model.classification_heads.mnli.out_proj.weight', 'classification_head.out_proj.weight'),
    ('model.classification_heads.mnli.out_proj.bias', 'classification_head.out_proj.bias'),
]







def 		A_							(						_lowerCAmelCase :     List[str]				):



      """simple docstring"""




      _lowerCamelCase					: List[Any]		    = [
          "encoder.version",
          "decoder.version",
          "model.encoder.version",
          "model.decoder.version",
          "_float_tensor",
      ]
      for k in ignore_keys:
            state_dict.pop(UpperCamelCase__					,						UpperCamelCase__				)







def 		A_							(						_lowerCAmelCase :     str					,						_lowerCAmelCase :     Tuple					,						_lowerCAmelCase :     List[Any]				):



      """simple docstring"""




      _lowerCamelCase					: Any		    = dct.pop(UpperCamelCase__				)
      _lowerCamelCase					: Dict		    = val







def 		A_							(						_lowerCAmelCase :     int				):



      """simple docstring"""




      _lowerCamelCase					: Tuple		    = torch.load(UpperCamelCase__					,						map_location="cpu"				)
      _lowerCamelCase					: List[Any]		    = torch.hub.load("pytorch/fairseq"					,						"bart.large.cnn"				).eval()
      hub_interface.model.load_state_dict(sd["model"]				)
      return hub_interface







def 		A_							(						_lowerCAmelCase :     str				):



      """simple docstring"""




      _lowerCamelCase    ,		_lowerCamelCase					: List[Any]		    = emb.weight.shape
      _lowerCamelCase					: List[Any]		    = nn.Linear(UpperCamelCase__					,						UpperCamelCase__					,						bias=UpperCamelCase__				)
      _lowerCamelCase					: Dict		    = emb.weight.data
      return lin_layer







@torch.no_grad()
def 		A_							(						_lowerCAmelCase :     List[str]					,						_lowerCAmelCase :     Tuple					,						_lowerCAmelCase :     Optional[int]=None				):



      """simple docstring"""




      if not os.path.exists(UpperCamelCase__				):
            _lowerCamelCase					: Any		    = torch.hub.load("pytorch/fairseq"					,						UpperCamelCase__				).eval()
      else:
            _lowerCamelCase					: List[str]		    = load_xsum_checkpoint(UpperCamelCase__				)

      bart.model.upgrade_state_dict(bart.model.state_dict()				)
      if hf_checkpoint_name is None:
            _lowerCamelCase					: Optional[int]		    = checkpoint_path.replace("."					,						"-"				)
      _lowerCamelCase					: Any		    = BartConfig.from_pretrained(UpperCamelCase__				)
      _lowerCamelCase					: int		    = bart.encode(UpperCamelCase__				).unsqueeze(0				)
      _lowerCamelCase					: Optional[Any]		    = BartTokenizer.from_pretrained(UpperCamelCase__				).encode(UpperCamelCase__					,						return_tensors="pt"				).unsqueeze(0				)
      if not torch.eq(UpperCamelCase__					,						UpperCamelCase__				).all():
            raise ValueError(
                F'converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}'				)

      if checkpoint_path == "bart.large.mnli":
            _lowerCamelCase					: Dict		    = bart.state_dict()
            remove_ignore_keys_(UpperCamelCase__				)
            _lowerCamelCase					: str		    = state_dict["model.decoder.embed_tokens.weight"]
            for src, dest in mnli_rename_keys:
                  rename_key(UpperCamelCase__					,						UpperCamelCase__					,						UpperCamelCase__				)
            _lowerCamelCase					: str		    = BartForSequenceClassification(UpperCamelCase__				).eval()
            model.load_state_dict(UpperCamelCase__				)
            _lowerCamelCase					: List[Any]		    = bart.predict("mnli"					,						UpperCamelCase__					,						return_logits=UpperCamelCase__				)
            _lowerCamelCase					: Dict		    = model(UpperCamelCase__				)[0]  # logits
      else:  # no classification heads to worry about
            _lowerCamelCase					: str		    = bart.model.state_dict()
            remove_ignore_keys_(UpperCamelCase__				)
            _lowerCamelCase					: Tuple		    = state_dict["decoder.embed_tokens.weight"]
            _lowerCamelCase					: Dict		    = bart.extract_features(UpperCamelCase__				)
            if hf_checkpoint_name == "facebook/bart-large":
                  _lowerCamelCase					: str		    = BartModel(UpperCamelCase__				).eval()
                  model.load_state_dict(UpperCamelCase__				)
                  _lowerCamelCase					: Tuple		    = model(UpperCamelCase__				).model[0]
            else:
                  _lowerCamelCase					: Union[str, Any]		    = BartForConditionalGeneration(UpperCamelCase__				).eval()  # an existing summarization ckpt
                  model.model.load_state_dict(UpperCamelCase__				)
                  if hasattr(UpperCamelCase__					,						"lm_head"				):
                        _lowerCamelCase					: Any		    = make_linear_from_emb(model.model.shared				)
                  _lowerCamelCase					: Tuple		    = model.model(UpperCamelCase__				)[0]

    # Check results
      if fairseq_output.shape != new_model_outputs.shape:
            raise ValueError(
                F'`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}'				)
      if (fairseq_output != new_model_outputs).any().item():
            raise ValueError("Some values in `fairseq_output` are different from `new_model_outputs`"				)
      Path(UpperCamelCase__				).mkdir(exist_ok=UpperCamelCase__				)
      model.save_pretrained(UpperCamelCase__				)


if __name__ == "__main__":
 UpperCAmelCase_							: List[Any] =							argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
 )
 parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 parser.add_argument(
     '--hf_config', default=None, type=str, help='Which huggingface architecture to use: bart-large-xsum'
 )
 UpperCAmelCase_							: Dict =							parser.parse_args()
 convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)
'''simple docstring'''




def __lowerCAmelCase					(				UpperCamelCase__							)      ->       float:
    __lowerCamelCase            =    0
    while len(UpperCamelCase__							) > 1:
        __lowerCamelCase            =    0
        # Consider two files with minimum cost to be merged
        for _ in range(2							):
            __lowerCamelCase            =    files.index(min(UpperCamelCase__							)							)
            temp += files[min_index]
            files.pop(UpperCamelCase__							)
        files.append(UpperCamelCase__							)
        optimal_merge_cost += temp
    return optimal_merge_cost


if __name__ == "__main__":
  import doctest

  doctest.testmod()
